Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

Vapor-deposited CsPbI3 solar cells demonstrate an efficiency of 16％

Hybrid organic-inorganic perovskite solar cells (PSCs) have made rapid progress in efficiency from 3.8％ to 25.5％ in the past decade [1-3].The hybrid perovskite ...     

Engineering of multiferroic BiFeO3 grain boundaries with head-to-head polarization configurations

Confined low dimensional charges with high density such as two-dimensional electron gas (2DEG) at interfaces and charged domain walls in ferroelectrics show gre...     

Magnetic circuit optimization and experimental study of new internally excited automatic balancing

Mass imbalance-induced vibration affects the rotating machinery very large,especially the high-speed types.Off-line balancing techniques have been widely develo...     

通过空间相似度设置阈值的Douglas-Peucker算法

线状地物的化简是地图综合的重要组成部分,Douglas-Peucker算法作为一种经典的化简线状地物的算法,化简时需要进行多次实验才能选取到合适的阈值,得到最终化简结果。为了避免选取阈值繁琐的过程,通过计算不同阈值下化简前后线状地物间的空间相似度,并将绝对阈值转换为相对阈值,利用曲线拟合的方法得到相对阈值和空间相似度之间的关系。基于两者之间的关系,在使用Douglas-Peucker算法化简线状地物时,即可通过空间相似度来选取合适的化简阈值,提高了化简的效率。     

总体最小二乘法及其在GNSS高程拟合中的应用

为了解决多项式曲面拟合方法在进行全球导航卫星系统(GNSS)高程拟合时,控制点的坐标数据与高程异常均存在测量误差的问题,从总体最小二乘法的角度,在系数矩阵存在误差的情况下,来优化GNSS高程拟合模型,提高拟合精度。实验结果表明：采用总体最小二乘法进行曲面拟合GNSS高程异常,较传统最小二乘法精度会有一定提高。     

Study on the fusion emotion classification of multiple characteristics based on attention mechanism

The current research on emotional classification uses many methods that combine the attention mechanism with neural networks.However,the effect is unsatisfactory when dealing with complex text.An emotional classification model is proposed,which combines multi-head attention(MHA)with improved structured-self attention(SSA).The model makes several different linear transformations of input by introducing MHA mechanism and can extract more comprehensive high-level phrase representation features from the word embedded vector.Meanwhile,it can realize the parallelization calculation and ensure the training speed of the model.The improved SSA structure uses matrices to represent different parts of a sentence to extract local key information,to ensure that the degree of dependence between words is not affected by time and sentence length,and generate the overall semantics of the sentence.Experiment results show that the current model effectively obtains global structural information and improves classification accuracy.     

Effects of Eco-Friendly Carrier on Low-Temperature Dyeing of Recycled Polyester Knit Fabrics

This work focused on effects of an eco-friendly carrier JYK on thermal properties,dye uptake,color yield(K/S values)and color fastness of recycled polyester kni...     

基于区间直觉模糊数ANP的体系贡献率计算方法

体系贡献率主要用于体系建设中度量装备对体系的贡献程度。为解决目前体系贡献率计算存在的一些问题,提出一种“任务–能力–指标–装备”的多层次装备指标体系结构。考虑了装备指标体系结构、网络分析法和区间直觉模糊数的特点,提出了一种基于区间直觉模糊数的网络分析法以得到更准确的体系贡献率。实验表明,该方法不仅能够解决体系贡献率计算公式现存的问题,而且与AHP(analytic hierarchy process)和ANP(analytic network process)相比,可以得到更准确的体系贡献率,为体系建设提供更有效的支持。     

基于仿真驱动的高速公路主动限速效用评价与推荐

传统的路侧被动限速方式对于特定的惩处区域以外缺少管控,间接导致车辆行为在时空上的不一致性甚至突变,影响了交通的通行效率与安全性。从车侧主动限速方式入手,提出主动限速效用评价与推荐方法,结合道路线形、交通流量、车型比例,开展多情景主动、被动限速交通仿真,利用安全间接分析模型及交通流运行状态,从安全与效率2个层面提取效用评价指标及其权重,采用集成学习方法进行预测分析。结果显示：主动限速方式相较于被动限速方式更有利于提高安全性和调节效率,而在主动限速方面,GBDT(gradient boosting decision tree)回归模型的预测稳定性和准确率更高(R²=0.984)。